Title:
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree
Author(s)
Kaess, Michael
Johannsson, Hordur
Roberts, Richard
Ila, Viorela
Leonard, John
Dellaert, Frank
Johannsson, Hordur
Roberts, Richard
Ila, Viorela
Leonard, John
Dellaert, Frank
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Abstract
We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of
existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique
tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the
square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three
insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in
terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple
editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm
for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental
variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of
iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent
mapping algorithms in both quality and efficiency.
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Date Issued
2012-02
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